CAIR - Center for Artificial Intelligence Research

Centre for Artificial Intelligence Research

Centre for Artificial Intelligence Research

CAIR is an Artificial Intelligence Research Centre. In our search for superintelligence, we attack unsolved problems and push the research frontier.

CAIR - Centre for Artificial Intelligence Research

Strategic research areas

Artificial morality and causality

Artificial morality and causality

Making machines that reason and act morally on the basis of causation rather than mere correlation

Machines that explore, experiment and learn

Machines that explore, experiment and learn

Using deep reinforcement learning to optimize hydropower production and other complex systems

Deep information understanding and reasoning

Deep information understanding and reasoning

Mining knowledge from medical records and other big data

Milestones and discoveries

TMComposites: Plug-and-Play Collaboration Between Specialized Tsetlin Machines

A new paper introduces the concept of Tsetlin Machine Composites. A Tsetlin Machine Composite merges multiple Tsetlin Machines with different specialities, significantly boosting team performance. The architecture is plug-and-play in that the individual Tsetlin Machines can train independently and then combine in any way, at any time, without fine-tuning. The architecture yields new state-of-the-art results for Tsetlin Machines on Fashion-MNIST, CIFAR-10, and CIFAR-100, using three image processing methods for demonstration.

Logical Word Embedding with Tsetlin Machine Autoencoder

Do we really need 300 floats to represent the meaning of a word? Representing words with words - a logical approach to word embedding using a self-supervised Tsetlin Machine Autoencoder.

A new self-supervised machine learning approach captures word meaning with concise logical expressions. The logical expressions consist of contextual words like "black," "cup," and "hot" to define other words like "coffee," thus being human-understandable. This logical embedding performs competitively on several intrinsic and extrinsic benchmarks, matching pre-trained GLoVe embeddings on six downstream classification tasks.

Affinity-Based Reinforcement Learning: A New Paradigm for Interpretability

Affinity-Based Reinforcement Learning: A New Paradigm for Interpretability

PhD Research Fellow at the University of Agder develops a novel interpretable reinforcement learning paradigm.

People

News

Proceedings of The First International Symposium on the Tsetlin Machine

Proceedings of The First International Symposium on the Tsetlin Machine Now Available!

Finally it is here! The proceedings of The First International Symposium on the Tsetlin Machine. Fourteen exciting papers advancing the Tsetlin machine research. Many thanks to the authors, the organizers, and all the supporters of the Tsetlin machine - we build this together. #democraticAI#greenAI #logicbasedAI #tsetlinmachine

Tsetlin Machine Boolean Bag-of-Words Boosted by GloVe Synonyms

Tsetlin Machine Interpretability and Accuracy in NLP Boosted Using GloVe Synonyms

By using GloVe to obtain synonyms, we have enhanced the Boolean Bag-of-Words used by Tsetlin Machines in NLP. The added synonyms help the Tsetlin Machine produce fewer and semantically more powerful rules, while boosting accuracy by up to 4%. This makes the transparent and interpretable rules more competitive with deep learning-based NLP models.

A machine learning approach for non-intrusive physiological and behavioral monitoring in mental healthcare settings.

A machine learning approach for non-intrusive physiological and behavioral monitoring in mental healthcare settings.

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